Reconciling solar forecasts: Probabilistic forecasting with homoscedastic Gaussian errors on a geographical hierarchy
暂无分享,去创建一个
[1] Jan Kleissl,et al. Operational solar forecasting for the real-time market , 2019, International Journal of Forecasting.
[2] Dazhi Yang,et al. Kriging for NSRDB PSM version 3 satellite-derived solar irradiance , 2018, Solar Energy.
[3] Gokhan Mert Yagli,et al. Ensemble kriging for environmental spatial processes , 2019, 2019 IEEE International Conference on Big Data (Big Data).
[4] Dipti Srinivasan,et al. Reconciling solar forecasts: Sequential reconciliation , 2019, Solar Energy.
[5] Qun Zhou,et al. Coherent Probabilistic Solar Power Forecasting , 2018, 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).
[6] Carlos D. Rodriguez-Gallegos,et al. Reconciling solar forecasts: Temporal hierarchy , 2017 .
[7] Galen Maclaurin,et al. The National Solar Radiation Data Base (NSRDB) , 2017, Renewable and Sustainable Energy Reviews.
[8] Dazhi Yang,et al. Post-processing of NWP forecasts using ground or satellite-derived data through kernel conditional density estimation , 2019, Journal of Renewable and Sustainable Energy.
[9] Dazhi Yang,et al. Very short term irradiance forecasting using the lasso , 2015 .
[10] J. Kleissl,et al. Reporting of irradiance modeling relative prediction errors , 2013 .
[11] Richard Perez,et al. Can we gauge forecasts using satellite-derived solar irradiance? , 2019, Journal of Renewable and Sustainable Energy.
[12] Hans-Georg Beyer,et al. Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[13] Dazhi Yang,et al. Making reference solar forecasts with climatology, persistence, and their optimal convex combination , 2019, Solar Energy.
[14] Joakim Widén,et al. Review on probabilistic forecasting of photovoltaic power production and electricity consumption , 2018 .
[15] J. Kleissl,et al. Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed , 2011 .
[16] Nan Chen,et al. Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging , 2013 .
[17] Abbas Khosravi,et al. A Survey of Computational Intelligence Techniques for Wind Power Uncertainty Quantification in Smart Grids , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[18] Charles F. Manski,et al. Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters , 2006 .
[19] Dipti Srinivasan,et al. Automatic hourly solar forecasting using machine learning models , 2019, Renewable and Sustainable Energy Reviews.
[20] Fotios Petropoulos,et al. Forecasting with temporal hierarchies , 2017, Eur. J. Oper. Res..
[21] Stefano Alessandrini,et al. An ultra-fast way of searching weather analogs for renewable energy forecasting , 2019, Solar Energy.
[22] Mathieu David,et al. Comparison of intraday probabilistic forecasting of solar irradiance using only endogenous data , 2018, International Journal of Forecasting.
[23] Rob J. Hyndman,et al. Optimal combination forecasts for hierarchical time series , 2011, Comput. Stat. Data Anal..
[24] Dazhi Yang,et al. Ultra-fast preselection in lasso-type spatio-temporal solar forecasting problems , 2018, Solar Energy.
[25] Dazhi Yang,et al. A correct validation of the National Solar Radiation Data Base (NSRDB) , 2018, Renewable and Sustainable Energy Reviews.
[26] Tilmann Gneiting,et al. Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings , 2015, 1503.08195.
[27] Dazhi Yang,et al. OpenSolar: Promoting the openness and accessibility of diverse public solar datasets , 2019, Solar Energy.
[28] A. Raftery,et al. Probabilistic forecasts, calibration and sharpness , 2007 .
[29] Pierre Pinson,et al. Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation— With Application to Solar Energy , 2016, IEEE Transactions on Power Systems.
[30] Ralf Mikut,et al. On the use of probabilistic forecasts in scheduling of renewable energy sources coupled to storages , 2018 .
[31] Dazhi Yang,et al. Spatial prediction using kriging ensemble , 2018, Solar Energy.
[32] Abbas Khosravi,et al. Uncertainty handling using neural network-based prediction intervals for electrical load forecasting , 2014 .
[33] Dazhi Yang,et al. Ensemble model output statistics as a probabilistic site-adaptation tool for satellite-derived and reanalysis solar irradiance , 2020 .
[34] T. Gneiting. Making and Evaluating Point Forecasts , 2009, 0912.0902.
[35] Dazhi Yang,et al. Reconciling solar forecasts: Geographical hierarchy , 2017 .
[36] Rob J. Hyndman,et al. Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization , 2018, Journal of the American Statistical Association.
[37] Jan Kleissl,et al. Editorial: Submission of Data Article is now open , 2018, Solar Energy.
[38] Dazhi Yang,et al. A universal benchmarking method for probabilistic solar irradiance forecasting , 2019, Solar Energy.
[39] George Athanasopoulos,et al. Hierarchical forecasts for Australian domestic tourism , 2009 .
[40] Dazhi Yang,et al. A guideline to solar forecasting research practice: Reproducible, operational, probabilistic or physically-based, ensemble, and skill (ROPES) , 2019, Journal of Renewable and Sustainable Energy.
[41] Dazhi Yang,et al. Ultra-fast analog ensemble using kd-tree , 2019, Journal of Renewable and Sustainable Energy.
[42] Rob J. Hyndman,et al. Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression , 2016, IEEE Transactions on Smart Grid.